自适应迭代广义解调分析方法及其在旋转机械故障诊断中的应用研究

Rotating machine vibration signals feature complex multi-components and nonstationarity under time-varying running conditions. How to effectively extract fault information from such complex and nonstationary signals is a common yet important topic in rotating machine fault diagnosis. In this project, with rotors and planetary gearboxes as the representative research target, and regarding the complexity and nonstationarity of their vibration signals, adaptive iterative generalized demodulation method will be investigated, and its application issues in rotating machine fault diagnosis will be addressed, through theoretical analysis, numerical simulation and experimental validation. Research topics include adaptive identification of true signal components, time-varying filtering within arbitrary time-frequency region, iterative mono-component decomposition, quality time-frequency analysis, enhanced order spectrum analysis and transient orbit analysis. By exploiting the unique advantages of adaptive iterative generalized demodulation method, the limitations inherent with most conventional methods will be overcome, the background noise will be suppressed, the influence due to subjective factors and the dependence on a priori knowledge will be avoided. The rich characteristic information contained in complex nonstationary vibration signals is expected to be effectively extracted, e.g. the fundamental yet key parameters such as amplitude, frequency, and orbit, as well as their instantaneous changes. Therefore, the fault vibration mechanism of rotating machine will be thoroughly revealed, and the health condition will be well identified.

旋转机械振动信号成分复杂，在时变工况下具有非平稳特点。如何从复杂非平稳振动信号中有效提取故障信息，是旋转机械故障诊断中的一个关键共性科学问题。以具有典型代表意义的转子和行星齿轮箱为研究对象，针对振动信号的成分复杂性以及时变工况下的非平稳特点，通过理论分析、仿真计算和实验研究等手段，研究自适应迭代广义解调信号分析方法，解决旋转机械故障诊断中涉及的复杂多分量非平稳振动信号分析理论、方法及应用层面的关键问题，包括信号真实分量的自适应辨识、任意时变带状时频通域滤波、单分量迭代分解、高性能时频分析、增强阶比分析和瞬态轴心轨迹分析等方法。挖掘发挥自适应迭代广义解调方法的独特优势，突破常规方法的局限，避免主观因素的影响以及对经验知识的依赖，抑制背景噪声干扰，有效分析复杂非平稳信号中蕴含的丰富信息，准确提取幅值、频率、轴心轨迹等参数的变化特征，全方位多角度揭示旋转机械的健康状态，识别故障原因。

工程实际中，旋转机械设备运行工况经常时变，信号具有复杂非平稳特点，其中蕴含设备健康状态的关键信息。项目以行星齿轮箱、轴承、转子等典型旋转机械为研究对象，结合系统动力学性质，通过信号解析建模，揭示了振动和电机电流信号的特征规律；深入研究了广义解调、代理检验、时变滤波等理论，提出了自适应迭代广义解调、高分辨率时频分析、增强阶比谱和瞬时全息谱等方法，解决了信号真实分量辨识、单分量分离、时频特征分析等问题，有效提取了复杂非平稳信号的频率、幅值及其时变特征信息，应用行星齿轮箱、行星轮轴承实验信号以及风电传动系统和水轮机现场实测信号进行了验证。发表学术论文19篇，其中SCI收录14篇，EI收录5篇；获授权国家发明专利3项；参加国际和国内重要学术会议6次，并作大会/邀请报告；获得2020教育部自然科学二等奖1项(项目负责人排名第1)；培养博士硕士研究生15人。